Lecture 19: Time series#
+Lecture 20: Time series#
UBC 2024-25
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diff --git a/lectures/notes/appendixA_feature-engineering-text-data.html b/lectures/notes/appendixA_feature-engineering-text-data.html index 60693ad4..d26062e4 100644 --- a/lectures/notes/appendixA_feature-engineering-text-data.html +++ b/lectures/notes/appendixA_feature-engineering-text-data.html @@ -66,7 +66,7 @@ - + @@ -196,7 +196,7 @@Simple feature engineering for our problem. Lecture 17: Recommender Systems
sklearn pipelines", "Lecture 6: sklearn ColumnTransformer and Text Features", "Lecture 7: Linear Models", "Lecture 8: Hyperparameter Optimization and Optimization Bias", "Lecture 9: Classification metrics", "Lecture 10: Regression metrics", "Lecture 12: Ensembles", "Lecture 13: Feature importances and model transparency", "Lecture 14: Feature engineering and feature selection", "Lecture 15: K-Means Clustering", "Lecture 16: More Clustering", "Section 101", "Section 102", "Lecture 3: ML Fundamentals Class Demo", "Lecture 4: Class demo", "Lecture 5 and 6: Class demo", "Lectures 7: Class demo", "Lectures 7: Class demo", "Lectures 8: Class demo", "Lectures 8: Class demo", "Lecture 9: Class demo", "Lecture 10: Class demo", "Lecture 12: Ensembles", "Lecture 13: Class demo", "Lecture 14: Class demo", "Lecture 15: Clustering class demo", "Lecture 15: Class demo", "Lecture 17: Class demo", "Lecture 18: Class demo", "Lecture 1", "Lecture 10", "Lecture 11", "Lecture 2", "Lecture 3", "Lecture 4", "Lecture 5", "Lecture 6", "Lecture 7", "Lecture 8", "Lecture 9", "Section 103", "Lecture 3 Demo: ML Fundamentals", "Lecture 1", "Lecture 11", "Lecture 2", "Lecture 3", "Lecture 4", "Lecture 5", "Lecture 6", "Lecture 7", "Lecture 8", "Lecture 9", "Lecture 1: Course Introduction", "Lecture 2: Terminology, Baselines, Decision Trees", "Lecture 3: Machine Learning Fundamentals", "Lecture 4: \\(k\\)-Nearest Neighbours and SVM RBFs", "Lecture 5: Preprocessing and sklearn pipelines", "Lecture 6: sklearn ColumnTransformer and Text Features", "Lecture 7: Linear Models", "Lecture 8: Hyperparameter Optimization and Optimization Bias", "Lecture 9: Classification metrics", "Lecture 10: Regression metrics", "Lecture 12: Ensembles", "Lecture 13: Feature importances and model transparency", "Lecture 14: Feature engineering and feature selection", "Lecture 15: K-Means Clustering", "Lecture 16: More Clustering", "Lecture 17: Recommender Systems", "Lecture 18: Introduction to natural language processing", "Lecture 19: Multi-class classification and introduction to computer vision", "Lecture 19: Time series", "Appendix A: Demo of feature engineering for text data", "Appendix B: Multi-class, meta-strategies", "Syllabus"], "titleterms": {"": [12, 14, 15, 16, 17, 20, 21, 23, 31, 36, 37, 39, 41, 57, 68, 70, 71, 72, 73, 76, 77, 79, 86], "0": [22, 78], "1": [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 34, 35, 38, 43, 45, 58, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85], "10": [21, 37, 46, 77], "101": 27, "102": 28, "103": 56, "11": [47, 59], "12": [22, 38, 78], "13": [23, 39, 79], "14": [24, 40, 80], "15": [25, 41, 42, 81], "16": [26, 82], "17": [43, 83], "18": [44, 84], "19": [85, 86], "2": [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23, 25, 26, 37, 48, 60, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 81, 82, 83], "2024w1": 1, "3": [12, 13, 14, 15, 16, 24, 25, 26, 29, 49, 57, 61, 68, 69, 70, 72, 80, 81, 82], "330": [1, 2, 3, 6, 8, 12], "340": [2, 12], "4": [12, 13, 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\ No newline at end of file
+Search.setIndex({"alltitles": {"": [[29, "id1"]], "(Optional) Changing the data": [[20, "optional-changing-the-data"], [76, "optional-changing-the-data"]], "(Optional) Evaluation metrics for multi-class classification": [[20, "optional-evaluation-metrics-for-multi-class-classification"], [76, "optional-evaluation-metrics-for-multi-class-classification"]], "(Optional) Example 1: Optimization bias": [[19, "optional-example-1-optimization-bias"], [75, "optional-example-1-optimization-bias"]], "(Optional) Example 2: Optimization bias": [[19, "optional-example-2-optimization-bias"], [75, "optional-example-2-optimization-bias"]], "(Optional) Fancier methods": [[19, "optional-fancier-methods"], [34, "optional-fancier-methods"], [35, "optional-fancier-methods"], [75, "optional-fancier-methods"]], "(Optional) Fitting in boosted regression trees.": [[22, "optional-fitting-in-boosted-regression-trees"], [38, "optional-fitting-in-boosted-regression-trees"], [78, "optional-fitting-in-boosted-regression-trees"]], "(Optional) Forward or backward selection": [[24, "optional-forward-or-backward-selection"], [80, "optional-forward-or-backward-selection"]], "(Optional) Macro average and weighted average": [[20, "optional-macro-average-and-weighted-average"], [76, "optional-macro-average-and-weighted-average"]], "(Optional) Parametric vs non parametric": [[15, "optional-parametric-vs-non-parametric"], [71, "optional-parametric-vs-non-parametric"]], "(Optional) Passing probability distributions to random search": [[19, "optional-passing-probability-distributions-to-random-search"], [34, "optional-passing-probability-distributions-to-random-search"], [35, "optional-passing-probability-distributions-to-random-search"], [75, "optional-passing-probability-distributions-to-random-search"]], "(Optional) Prediction in boosted regression trees": [[22, "optional-prediction-in-boosted-regression-trees"], [38, "optional-prediction-in-boosted-regression-trees"], [78, "optional-prediction-in-boosted-regression-trees"]], "(Optional) Problems with feature selection": [[24, "optional-problems-with-feature-selection"], [80, "optional-problems-with-feature-selection"]], "(Optional) Search and score": [[24, "optional-search-and-score"], [80, "optional-search-and-score"]], "(Optional) Searching for optimal parameters with successive halving\u00b6": [[19, "optional-searching-for-optimal-parameters-with-successive-halving"], [34, "optional-searching-for-optimal-parameters-with-successive-halving"], [35, "optional-searching-for-optimal-parameters-with-successive-halving"], [75, "optional-searching-for-optimal-parameters-with-successive-halving"]], "(Optional) Some more details": [[20, "optional-some-more-details"], [36, "optional-some-more-details"], [76, "optional-some-more-details"]], "(Supervised) machine learning: popular definition": [[12, "supervised-machine-learning-popular-definition"], [68, "supervised-machine-learning-popular-definition"]], "(iClicker) Exercise 14.1": [[24, "id1"], [80, "id1"]], "(iClicker) Exercise 3.1": [[15, "iclicker-exercise-3-1"]], "(iClicker) Exercise 3.2": [[15, "iclicker-exercise-3-2"]], "(iClicker) Exercise 4.1": [[71, "iclicker-exercise-4-1"]], "(iClicker) Exercise 4.2": [[71, "iclicker-exercise-4-2"]], "(iClicker) Exercise 5.1": [[16, "iclicker-exercise-5-1"], [72, "iclicker-exercise-5-1"]], "(iClicker) Exercise 5.2": [[16, "iclicker-exercise-5-2"], [72, "iclicker-exercise-5-2"]], "(iClicker) Exercise 5.3": [[16, "iclicker-exercise-5-3"], [72, "iclicker-exercise-5-3"]], "(iClicker) Exercise 6.1": [[17, "iclicker-exercise-6-1"], [73, "iclicker-exercise-6-1"]], "(iClicker) Exercise 6.2": [[17, "iclicker-exercise-6-2"], [73, "iclicker-exercise-6-2"]], "(iClicker) Exercise 7.1": [[18, "iclicker-exercise-7-1"], [74, "iclicker-exercise-7-1"]], "(iClicker) Exercise 7.2": [[18, "iclicker-exercise-7-2"], [74, "iclicker-exercise-7-2"]], "(iClicker) Exercise 8.1": [[19, "iclicker-exercise-8-1"], [75, "iclicker-exercise-8-1"]], "(iClicker) Midterm poll": [[25, "iclicker-midterm-poll"], [81, "iclicker-midterm-poll"]], "15.1 Select all of the following statements which are True (iClicker)": [[25, "select-all-of-the-following-statements-which-are-true-iclicker"], [81, "select-all-of-the-following-statements-which-are-true-iclicker"]], "15.2 Select all of the following statements which are True (iClicker)": [[25, "id1"], [81, "id1"]], "15.3 Select all of the following statements which are True (iClicker)": [[25, "id3"], [81, "id3"]], "16.1 Select all of the following statements which are True (iClicker)": [[26, "select-all-of-the-following-statements-which-are-true-iclicker"], [82, "select-all-of-the-following-statements-which-are-true-iclicker"]], "16.2 Select all of the following statements which are True (iClicker)": [[26, "id2"], [82, "id2"]], "16.3 Select all of the following statements which are True": [[26, "select-all-of-the-following-statements-which-are-true"], [82, "select-all-of-the-following-statements-which-are-true"]], "A few comments on PR curve": [[20, "a-few-comments-on-pr-curve"], [36, "a-few-comments-on-pr-curve"], [76, "a-few-comments-on-pr-curve"]], "A few comments on clustering evaluation": [[26, "a-few-comments-on-clustering-evaluation"], [82, "a-few-comments-on-clustering-evaluation"]], "AP score": [[20, "ap-score"], [36, "ap-score"], [76, "ap-score"]], "AP vs. F1-score": [[20, "ap-vs-f1-score"], [36, "ap-vs-f1-score"], [76, "ap-vs-f1-score"]], "AUC or AP?": [[20, "auc-or-ap"]], "About this course": [[12, "about-this-course"]], "About this document": [[8, "about-this-document"]], "Academic concessions": [[89, "academic-concessions"]], "Accessing homework assignments": [[7, "accessing-homework-assignments"]], "Accessing learned parameters": [[18, "accessing-learned-parameters"], [74, "accessing-learned-parameters"]], "Activity": [[12, "activity"]], "Activity (~4 mins)": [[39, "activity-4-mins"]], "Activity (~5 mins)": [[23, "activity-5-mins"], [23, "id3"], [79, "activity-5-mins"], [79, "id3"]], "Activity: Context and word meaning": [[44, "activity-context-and-word-meaning"], [84, "activity-context-and-word-meaning"]], "Activity: Discuss the following questions in your group": [[29, "activity-discuss-the-following-questions-in-your-group"], [57, "activity-discuss-the-following-questions-in-your-group"], [57, "id1"], [57, "id2"]], "Activity: How can you measure quality of the data? (~3 mins)": [[24, "activity-how-can-you-measure-quality-of-the-data-3-mins"], [80, "activity-how-can-you-measure-quality-of-the-data-3-mins"]], "Adding/removing columns with [] and drop()": [[8, "adding-removing-columns-with-and-drop"]], "Adding/removing rows with [] and drop()": [[8, "adding-removing-rows-with-and-drop"]], "Additional submission instructions": [[7, "additional-submission-instructions"]], "Addressing class imbalance": [[20, "addressing-class-imbalance"], [76, "addressing-class-imbalance"]], "Advantages of RandomizedSearchCV": [[19, "advantages-of-randomizedsearchcv"], [19, "id1"], [34, "advantages-of-randomizedsearchcv"], [34, "id1"], [35, "advantages-of-randomizedsearchcv"], [35, "id1"], [75, "advantages-of-randomizedsearchcv"], [75, "id1"]], "Alternative and more compact syntax: make_pipeline": [[16, "alternative-and-more-compact-syntax-make-pipeline"], [72, "alternative-and-more-compact-syntax-make-pipeline"]], "Alternative methods for scaling": [[31, "alternative-methods-for-scaling"]], "Alternative terminology for examples, features, targets, and training": [[13, "alternative-terminology-for-examples-features-targets-and-training"], [69, "alternative-terminology-for-examples-features-targets-and-training"]], "An effective strategy": [[22, "an-effective-strategy"], [38, "an-effective-strategy"], [78, "an-effective-strategy"]], "An example from a project": [[87, "an-example-from-a-project"]], "An example of a bootstrap samples": [[22, "an-example-of-a-bootstrap-samples"], [38, "an-example-of-a-bootstrap-samples"], [78, "an-example-of-a-bootstrap-samples"]], "Analogy-based algorithms in practice": [[15, "analogy-based-algorithms-in-practice"], [71, "analogy-based-algorithms-in-practice"]], "Analogy-based models": [[15, "analogy-based-models"], [71, "analogy-based-models"]], "Announcements": [[74, "announcements"]], "Appendix A: Demo of feature engineering for text data": [[87, null]], "Appendix B: Multi-class, meta-strategies": [[88, null]], "Applying feature transformations": [[21, "applying-feature-transformations"], [37, "applying-feature-transformations"], [77, "applying-feature-transformations"]], "Applying functions to a dataframe with df.apply() and df.applymap()": [[8, "applying-functions-to-a-dataframe-with-df-apply-and-df-applymap"]], "Are we doing better with class_weight=\"balanced\"?": [[20, "are-we-doing-better-with-class-weight-balanced"], [76, "are-we-doing-better-with-class-weight-balanced"]], "Area under the curve (AUC)": [[20, "area-under-the-curve-auc"], [36, "area-under-the-curve-auc"], [76, "area-under-the-curve-auc"]], "Assessing on the test set": [[29, "assessing-on-the-test-set"], [57, "assessing-on-the-test-set"]], "Assignments": [[89, "assignments"]], "Attention": [[13, null], [13, null], [13, null], [15, null], [69, null], [69, null], [69, null], [71, null]], "Automated hyperparameter optimization": [[19, "automated-hyperparameter-optimization"], [19, "id3"], [75, "automated-hyperparameter-optimization"], [75, "id3"]], "Averaging": [[22, "averaging"], [38, "averaging"], [78, "averaging"]], "Bad range for hyperparameters": [[19, "bad-range-for-hyperparameters"], [34, "bad-range-for-hyperparameters"], [35, "bad-range-for-hyperparameters"], [75, "bad-range-for-hyperparameters"]], "Bag of words (BOW) representation": [[17, "bag-of-words-bow-representation"], [73, "bag-of-words-bow-representation"]], "Bag-of-words model": [[87, "bag-of-words-model"]], "Baseline": [[20, "baseline"], [23, "baseline"], [36, "baseline"], [39, "baseline"], [76, "baseline"], [79, "baseline"]], "Baseline Approaches": [[43, "baseline-approaches"], [83, "baseline-approaches"]], "Baseline model": [[29, "baseline-model"], [57, "baseline-model"]], "Baselines": [[13, "baselines"], [22, "baselines"], [38, "baselines"], [69, "baselines"], [78, "baselines"]], "Baselines [video]": [[13, "baselines-video"], [69, "baselines-video"]], "Basic text preprocessing [video]": [[44, "basic-text-preprocessing-video"], [84, "basic-text-preprocessing-video"]], "Better features usually help more than a better model.": [[24, "better-features-usually-help-more-than-a-better-model"], [80, "better-features-usually-help-more-than-a-better-model"]], "Beyond error rate in recommendation systems": [[43, "beyond-error-rate-in-recommendation-systems"], [83, "beyond-error-rate-in-recommendation-systems"]], "Bias vs variance tradeoff": [[14, "bias-vs-variance-tradeoff"], [70, "bias-vs-variance-tradeoff"]], "Big picture and datasets": [[13, "big-picture-and-datasets"], [69, "big-picture-and-datasets"]], "Big picture and motivation": [[14, "big-picture-and-motivation"], [70, "big-picture-and-motivation"]], "Books": [[1, "books"]], "Break (5 min)": [[8, "break-5-min"], [12, "break-5-min"], [13, "break-5-min"], [14, "break-5-min"], [15, "break-5-min"], [16, "break-5-min"], [17, "break-5-min"], [24, "break-5-min"], [44, "break-5-min"], [69, "break-5-min"], [70, "break-5-min"], [71, "break-5-min"], [72, "break-5-min"], [73, "break-5-min"], [80, "break-5-min"], [84, "break-5-min"], [85, "break-5-min"]], "Broadcasting in numpy": [[8, "broadcasting-in-numpy"]], "Building a supervise machine learning model": [[12, "building-a-supervise-machine-learning-model"], [68, "building-a-supervise-machine-learning-model"]], "Building decision trees with sklearn": [[13, "building-decision-trees-with-sklearn"], [69, "building-decision-trees-with-sklearn"]], "Building user profiles": [[43, "building-user-profiles"], [83, "building-user-profiles"]], "CPSC 330 Documents": [[3, null]], "CPSC 330 Python notes": [[8, null]], "CPSC 330 grading policies": [[6, null]], "CPSC 330 vs. 340": [[12, "cpsc-330-vs-340"]], "CPSC 330 vs. CPSC 340": [[2, null]], "Can we learn without targets?": [[25, "can-we-learn-without-targets"], [81, "can-we-learn-without-targets"]], "Can we use this feature in the model?": [[16, "can-we-use-this-feature-in-the-model"], [72, "can-we-use-this-feature-in-the-model"]], "Cases where it\u2019s OK to break the golden rule": [[17, "cases-where-it-s-ok-to-break-the-golden-rule"], [73, "cases-where-it-s-ok-to-break-the-golden-rule"]], "CatBoost": [[22, "catboost"], [38, "catboost"], [78, "catboost"]], "Categorical features": [[23, "categorical-features"], [31, "categorical-features"], [39, "categorical-features"], [79, "categorical-features"]], "Categorical features [video]": [[16, "categorical-features-video"], [72, "categorical-features-video"]], "Categorical features with only two possible categories": [[17, "categorical-features-with-only-two-possible-categories"], [73, "categorical-features-with-only-two-possible-categories"]], "Centre for Accessibility (CfA) Exam Accommodations": [[89, "centre-for-accessibility-cfa-exam-accommodations"]], "Changing the training procedure": [[20, "changing-the-training-procedure"], [76, "changing-the-training-procedure"]], "Characters in this course?": [[12, "characters-in-this-course"], [68, "characters-in-this-course"]], "Checklist for you before next class": [[12, "checklist-for-you-before-next-class"]], "Choosing K [video]": [[25, "choosing-k-video"], [81, "choosing-k-video"]], "Choosing n_neighbors": [[15, "choosing-n-neighbors"], [71, "choosing-n-neighbors"]], "Citing sources": [[7, "citing-sources"]], "Class imbalance in training sets": [[20, "class-imbalance-in-training-sets"], [76, "class-imbalance-in-training-sets"]], "Class meetings": [[89, "class-meetings"]], "Classification report": [[20, "classification-report"], [36, "classification-report"], [76, "classification-report"]], "Classification vs. Regression": [[13, "classification-vs-regression"], [69, "classification-vs-regression"]], "Classification with KNeighborsClassifier": [[30, "classification-with-kneighborsclassifier"]], "Clustering Activity (~5 mins)": [[25, "clustering-activity-5-mins"], [81, "clustering-activity-5-mins"]], "Clustering motivation [video]": [[25, "clustering-motivation-video"], [81, "clustering-motivation-video"]], "Clustering: Input and (possible) output": [[25, "clustering-input-and-possible-output"], [81, "clustering-input-and-possible-output"]], "Code of conduct": [[89, "code-of-conduct"]], "Coefficients and intercept": [[18, "coefficients-and-intercept"], [74, "coefficients-and-intercept"]], "ColumnTransformer example": [[17, "columntransformer-example"], [73, "columntransformer-example"]], "ColumnTransformer on the California housing dataset": [[17, "columntransformer-on-the-california-housing-dataset"], [73, "columntransformer-on-the-california-housing-dataset"]], "ColumnTransformer: Transformed data": [[17, "columntransformer-transformed-data"], [73, "columntransformer-transformed-data"]], "Coming up \u2026": [[14, "coming-up"], [70, "coming-up"]], "Coming up:": [[15, "coming-up"], [71, "coming-up"]], "Command-line git": [[5, "command-line-git"]], "Common applications": [[25, "common-applications"], [81, "common-applications"]], "Common preprocessing techniques": [[16, "common-preprocessing-techniques"], [72, "common-preprocessing-techniques"]], "Communications": [[12, "communications"]], "Completing the utility matrix with content-based filtering": [[43, "completing-the-utility-matrix-with-content-based-filtering"], [83, "completing-the-utility-matrix-with-content-based-filtering"]], "Components of a linear classifier": [[18, "components-of-a-linear-classifier"], [74, "components-of-a-linear-classifier"]], "Concepts we revised in this demo": [[29, "concepts-we-revised-in-this-demo"], [57, "concepts-we-revised-in-this-demo"]], "Confusion matrix": [[36, "confusion-matrix"], [76, "confusion-matrix"]], "Confusion matrix (video)": [[20, "confusion-matrix-video"]], "Confusion matrix with cross-validation": [[20, "confusion-matrix-with-cross-validation"], [36, "confusion-matrix-with-cross-validation"], [76, "confusion-matrix-with-cross-validation"]], "Cons of k-NNs for supervised learning": [[15, "cons-of-k-nns-for-supervised-learning"], [71, "cons-of-k-nns-for-supervised-learning"]], "Content-based filtering": [[43, "content-based-filtering"], [83, "content-based-filtering"]], "Convenient make_column_transformer syntax": [[17, "convenient-make-column-transformer-syntax"], [73, "convenient-make-column-transformer-syntax"]], "Course Learning Objectives": [[11, null]], "Course co-ordinator": [[1, "course-co-ordinator"], [89, "course-co-ordinator"]], "Course description": [[89, "course-description"]], "Course format": [[12, "course-format"]], "Course website": [[12, "course-website"]], "Create X and y": [[13, "create-x-and-y"], [69, "create-x-and-y"]], "Create a classifier object": [[13, "create-a-classifier-object"], [69, "create-a-classifier-object"]], "Create a column transformer": [[17, "create-a-column-transformer"], [73, "create-a-column-transformer"]], "Creating train_df and test_df": [[14, "creating-train-df-and-test-df"], [70, "creating-train-df-and-test-df"]], "Creating utility matrix": [[43, "creating-utility-matrix"], [83, "creating-utility-matrix"]], "Credit": [[10, "credit"]], "Cross validation with different metrics": [[20, "cross-validation-with-different-metrics"], [36, "cross-validation-with-different-metrics"], [76, "cross-validation-with-different-metrics"]], "Cross-validation": [[29, "cross-validation"], [57, "cross-validation"], [86, "cross-validation"], [86, "id4"]], "Cross-validation [video]": [[14, "cross-validation-video"], [70, "cross-validation-video"]], "Cross-validation to the rescue!!": [[14, "cross-validation-to-the-rescue"], [70, "cross-validation-to-the-rescue"]], "Cross-validation using scikit-learn": [[14, "cross-validation-using-scikit-learn"], [70, "cross-validation-using-scikit-learn"]], "Curse of dimensionality": [[15, "curse-of-dimensionality"], [71, "curse-of-dimensionality"]], "Customer segmentation": [[25, "customer-segmentation"], [81, "customer-segmentation"]], "DBSCAN": [[42, "dbscan"]], "DBSCAN [video]": [[26, "dbscan-video"], [82, "dbscan-video"]], "DBSCAN introduction": [[26, "dbscan-introduction"], [82, "dbscan-introduction"]], "DBSCAN: failure cases": [[26, "dbscan-failure-cases"], [26, "id1"], [82, "dbscan-failure-cases"], [82, "id1"]], "Data": [[17, "data"], [18, "data"], [22, "data"], [23, "data"], [23, "id1"], [38, "data"], [39, "data"], [39, "id1"], [73, "data"], [74, "data"], [78, "data"], [79, "data"], [79, "id1"]], "Data Splitting [video]": [[14, "data-splitting-video"], [70, "data-splitting-video"]], "Data and main approaches": [[43, "data-and-main-approaches"], [83, "data-and-main-approaches"]], "Data and splitting": [[31, "data-and-splitting"]], "Data exploration": [[25, "data-exploration"], [81, "data-exploration"]], "Data splitting": [[29, "data-splitting"], [57, "data-splitting"]], "Dataframe summaries": [[8, "dataframe-summaries"]], "Dataset": [[85, "dataset"]], "Dataset [video]": [[21, "dataset-video"], [37, "dataset-video"], [77, "dataset-video"]], "Dataset for demonstration": [[20, "dataset-for-demonstration"], [36, "dataset-for-demonstration"], [76, "dataset-for-demonstration"]], "Dataset, splitting, and baseline": [[16, "dataset-splitting-and-baseline"], [72, "dataset-splitting-and-baseline"]], "Datasets": [[7, "datasets"]], "Dealing with class imbalance [video]": [[20, "dealing-with-class-imbalance-video"], [76, "dealing-with-class-imbalance-video"]], "Dealing with unknown categories": [[17, "dealing-with-unknown-categories"], [73, "dealing-with-unknown-categories"]], "Debugging": [[10, "debugging"]], "Decision boundaries playground": [[30, "decision-boundaries-playground"]], "Decision boundary": [[13, "decision-boundary"], [69, "decision-boundary"]], "Decision boundary for max_depth=1": [[13, "decision-boundary-for-max-depth-1"], [69, "decision-boundary-for-max-depth-1"]], "Decision boundary for max_depth=2": [[13, "decision-boundary-for-max-depth-2"], [69, "decision-boundary-for-max-depth-2"]], "Decision boundary for max_depth=5": [[13, "decision-boundary-for-max-depth-5"], [69, "decision-boundary-for-max-depth-5"]], "Decision boundary of SVMs": [[15, "decision-boundary-of-svms"], [71, "decision-boundary-of-svms"]], "Decision boundary of logistic regression": [[18, "decision-boundary-of-logistic-regression"], [74, "decision-boundary-of-logistic-regression"]], "Decision tree algorithm": [[13, "decision-tree-algorithm"], [69, "decision-tree-algorithm"]], "Decision tree feature importances": [[23, "decision-tree-feature-importances"], [39, "decision-tree-feature-importances"], [79, "decision-tree-feature-importances"]], "Decision tree for regression problems": [[13, "decision-tree-for-regression-problems"], [69, "decision-tree-for-regression-problems"]], "Decision tree model": [[29, "decision-tree-model"], [57, "decision-tree-model"]], "Decision tree with max_depth=1": [[13, "decision-tree-with-max-depth-1"], [69, "decision-tree-with-max-depth-1"]], "Decision tree with max_depth=3": [[13, "decision-tree-with-max-depth-3"], [69, "decision-tree-with-max-depth-3"]], "Decision trees [video]": [[13, "decision-trees-video"], [69, "decision-trees-video"]], "Decision trees with continuous features": [[13, "decision-trees-with-continuous-features"], [69, "decision-trees-with-continuous-features"]], "DecisionTreeClassifier baseline": [[22, "decisiontreeclassifier-baseline"], [38, "decisiontreeclassifier-baseline"], [78, "decisiontreeclassifier-baseline"]], "DecisionTreeClassifier on quiz2 grade prediction toy dataset": [[13, "decisiontreeclassifier-on-quiz2-grade-prediction-toy-dataset"], [69, "decisiontreeclassifier-on-quiz2-grade-prediction-toy-dataset"]], "Decreasing the threshold": [[20, "decreasing-the-threshold"], [36, "decreasing-the-threshold"], [76, "decreasing-the-threshold"]], "Deep learning": [[86, "deep-learning"]], "Deep learning software": [[85, "deep-learning-software"]], "Deliverable due dates (tentative)": [[1, "deliverable-due-dates-tentative"]], "Demo of feature engineering with numeric features": [[24, "demo-of-feature-engineering-with-numeric-features"], [40, "demo-of-feature-engineering-with-numeric-features"], [80, "demo-of-feature-engineering-with-numeric-features"]], "Demo: A more complicated dataset": [[86, "demo-a-more-complicated-dataset"]], "Demo: Model interpretation of linear classifiers": [[32, "demo-model-interpretation-of-linear-classifiers"], [33, "demo-model-interpretation-of-linear-classifiers"]], "Dendrogram": [[26, "dendrogram"], [82, "dendrogram"]], "Different models": [[23, "different-models"], [39, "different-models"], [79, "different-models"]], "Different range for hyperparameters yields better results!": [[19, "different-range-for-hyperparameters-yields-better-results"], [34, "different-range-for-hyperparameters-yields-better-results"], [35, "different-range-for-hyperparameters-yields-better-results"], [75, "different-range-for-hyperparameters-yields-better-results"]], "Different scoring functions with cross_validate": [[21, "different-scoring-functions-with-cross-validate"], [37, "different-scoring-functions-with-cross-validate"], [77, "different-scoring-functions-with-cross-validate"]], "Dimensions in ML problems": [[15, "dimensions-in-ml-problems"], [71, "dimensions-in-ml-problems"]], "Discuss the following questions in your group": [[29, "discuss-the-following-questions-in-your-group"], [57, "discuss-the-following-questions-in-your-group"]], "Discussion question": [[44, "discussion-question"], [84, "discussion-question"]], "Discussion questions": [[31, "discussion-questions"]], "Distance between feature vectors": [[15, "distance-between-feature-vectors"], [71, "distance-between-feature-vectors"]], "Do we actually want to use certain features for prediction?": [[17, "do-we-actually-want-to-use-certain-features-for-prediction"], [73, "do-we-actually-want-to-use-certain-features-for-prediction"]], "Do we have class imbalance?": [[22, "do-we-have-class-imbalance"], [23, "do-we-have-class-imbalance"], [38, "do-we-have-class-imbalance"], [39, "do-we-have-class-imbalance"], [78, "do-we-have-class-imbalance"], [79, "do-we-have-class-imbalance"]], "Do we have correlated features?": [[23, "do-we-have-correlated-features"], [39, "do-we-have-correlated-features"], [79, "do-we-have-correlated-features"]], "Document clustering": [[25, "document-clustering"], [81, "document-clustering"]], "Domain-specific transformations": [[24, "domain-specific-transformations"], [80, "domain-specific-transformations"]], "Dummy Classifier": [[31, "dummy-classifier"]], "Dummy classifier": [[87, "dummy-classifier"]], "Dummy model": [[30, "dummy-model"]], "DummyClassifier": [[13, "dummyclassifier"], [69, "dummyclassifier"], [86, "dummyclassifier"]], "DummyClassifier baseline": [[22, "dummyclassifier-baseline"], [38, "dummyclassifier-baseline"], [78, "dummyclassifier-baseline"]], "DummyClassifier on quiz2 grade prediction toy dataset": [[13, "dummyclassifier-on-quiz2-grade-prediction-toy-dataset"], [69, "dummyclassifier-on-quiz2-grade-prediction-toy-dataset"]], "DummyRegressor": [[13, "dummyregressor"], [21, "dummyregressor"], [37, "dummyregressor"], [69, "dummyregressor"], [77, "dummyregressor"]], "EDA": [[16, "eda"], [20, "eda"], [21, "eda"], [36, "eda"], [37, "eda"], [72, "eda"], [76, "eda"], [77, "eda"]], "Encoding text data": [[73, "encoding-text-data"]], "Encoding text data (Video)": [[17, "encoding-text-data-video"]], "Encoding time as a number": [[86, "encoding-time-as-a-number"]], "Encoding time of day as a categorical feature": [[86, "encoding-time-of-day-as-a-categorical-feature"]], "Euclidean distance": [[15, "euclidean-distance"], [71, "euclidean-distance"]], "Evaluating DBSCAN clusters": [[26, "evaluating-dbscan-clusters"], [82, "evaluating-dbscan-clusters"]], "Evaluation": [[43, "evaluation"], [43, "id3"], [83, "evaluation"], [83, "id3"]], "Evaluation metrics for binary classification: Motivation": [[20, "evaluation-metrics-for-binary-classification-motivation"], [36, "evaluation-metrics-for-binary-classification-motivation"], [76, "evaluation-metrics-for-binary-classification-motivation"]], "Evalution metrics overview": [[20, "evalution-metrics-overview"], [36, "evalution-metrics-overview"], [76, "evalution-metrics-overview"]], "Examining learned coefficients": [[32, "examining-learned-coefficients"], [33, "examining-learned-coefficients"]], "Examining the preprocessed data": [[21, "examining-the-preprocessed-data"], [37, "examining-the-preprocessed-data"], [77, "examining-the-preprocessed-data"]], "Examining the vocabulary": [[32, "examining-the-vocabulary"], [33, "examining-the-vocabulary"]], "Example": [[18, "example"], [22, "example"], [38, "example"], [74, "example"], [78, "example"]], "Example 1": [[23, "example-1"]], "Example 1: Predicting whether a patient has a liver disease or not": [[12, "example-1-predicting-whether-a-patient-has-a-liver-disease-or-not"], [68, "example-1-predicting-whether-a-patient-has-a-liver-disease-or-not"]], "Example 1: What is \u201ccorrect\u201d grouping?": [[25, "example-1-what-is-correct-grouping"], [81, "example-1-what-is-correct-grouping"]], "Example 1: quiz 2 grade prediction": [[13, "example-1-quiz-2-grade-prediction"], [69, "example-1-quiz-2-grade-prediction"]], "Example 2": [[23, "example-2"]], "Example 2: Predicting country using the longitude and latitude": [[13, "example-2-predicting-country-using-the-longitude-and-latitude"], [69, "example-2-predicting-country-using-the-longitude-and-latitude"]], "Example 2: Predicting the label of a given image": [[12, "example-2-predicting-the-label-of-a-given-image"], [68, "example-2-predicting-the-label-of-a-given-image"]], "Example 3: Predicting sentiment expressed in a movie review": [[12, "example-3-predicting-sentiment-expressed-in-a-movie-review"], [68, "example-3-predicting-sentiment-expressed-in-a-movie-review"]], "Example 4: Predicting housing prices": [[12, "example-4-predicting-housing-prices"], [68, "example-4-predicting-housing-prices"]], "Example showing how can we interpret coefficients of scaled features.": [[23, "example-showing-how-can-we-interpret-coefficients-of-scaled-features"], [39, "example-showing-how-can-we-interpret-coefficients-of-scaled-features"], [79, "example-showing-how-can-we-interpret-coefficients-of-scaled-features"]], "Example: Is \u201cRelevance\u201d clearly defined?": [[24, "example-is-relevance-clearly-defined"], [80, "example-is-relevance-clearly-defined"]], "Example: Predict whether a message is spam or not": [[12, "example-predict-whether-a-message-is-spam-or-not"], [68, "example-predict-whether-a-message-is-spam-or-not"]], "Example: Supervised vs unsupervised learning": [[25, "example-supervised-vs-unsupervised-learning"], [81, "example-supervised-vs-unsupervised-learning"]], "Example: Tabular data for grade prediction": [[13, "example-tabular-data-for-grade-prediction"], [69, "example-tabular-data-for-grade-prediction"]], "Example: Tabular data for the housing price prediction": [[13, "example-tabular-data-for-the-housing-price-prediction"], [69, "example-tabular-data-for-the-housing-price-prediction"]], "Example: class_weight parameter of sklearn LogisticRegression": [[20, "example-class-weight-parameter-of-sklearn-logisticregression"], [76, "example-class-weight-parameter-of-sklearn-logisticregression"]], "Example: k-nearest neighbours on the Spotify dataset": [[16, "example-k-nearest-neighbours-on-the-spotify-dataset"], [72, "example-k-nearest-neighbours-on-the-spotify-dataset"]], "Examples": [[12, "examples"], [68, "examples"]], "Exercise 17.1 Select all of the following statements which are True (iClicker)": [[43, "exercise-17-1-select-all-of-the-following-statements-which-are-true-iclicker"], [43, "id2"], [83, "exercise-17-1-select-all-of-the-following-statements-which-are-true-iclicker"]], "Exercise 17.2 Select all of the following statements which are True (iClicker)": [[83, "exercise-17-2-select-all-of-the-following-statements-which-are-true-iclicker"]], "Exercise 2.1 Select all of the following statements which are examples of supervised machine learning": [[13, "exercise-2-1-select-all-of-the-following-statements-which-are-examples-of-supervised-machine-learning"], [69, "exercise-2-1-select-all-of-the-following-statements-which-are-examples-of-supervised-machine-learning"]], "Exercise 2.4": [[13, "exercise-2-4"], [69, "exercise-2-4"]], "Exercise 8.2": [[19, "exercise-8-2"], [75, "exercise-8-2"]], "Exhaustive grid search: sklearn.model_selection.GridSearchCV": [[19, "exhaustive-grid-search-sklearn-model-selection-gridsearchcv"], [34, "exhaustive-grid-search-sklearn-model-selection-gridsearchcv"], [35, "exhaustive-grid-search-sklearn-model-selection-gridsearchcv"], [75, "exhaustive-grid-search-sklearn-model-selection-gridsearchcv"]], "Explaining a prediction": [[23, "explaining-a-prediction"], [39, "explaining-a-prediction"], [79, "explaining-a-prediction"]], "Exploratory Data Analysis": [[29, "exploratory-data-analysis"], [57, "exploratory-data-analysis"]], "Exploratory data analysis": [[31, "exploratory-data-analysis"], [86, "exploratory-data-analysis"]], "Extracting BOW features using scikit-learn": [[17, "extracting-bow-features-using-scikit-learn"], [73, "extracting-bow-features-using-scikit-learn"]], "Extracting date and time information": [[86, "extracting-date-and-time-information"]], "F1-score": [[20, "f1-score"], [36, "f1-score"], [76, "f1-score"]], "Faster method: vectorize the loop over rows": [[8, "faster-method-vectorize-the-loop-over-rows"]], "Fastest method: broadcasting": [[8, "fastest-method-broadcasting"]], "Feature crosses for one-hot encoded features": [[24, "feature-crosses-for-one-hot-encoded-features"], [40, "feature-crosses-for-one-hot-encoded-features"], [80, "feature-crosses-for-one-hot-encoded-features"]], "Feature engineering": [[86, "feature-engineering"]], "Feature engineering for date/time columns": [[86, "feature-engineering-for-date-time-columns"]], "Feature engineering: Encoding date/time as feature(s)": [[86, "feature-engineering-encoding-date-time-as-feature-s"]], "Feature engineering: Motivation": [[24, "feature-engineering-motivation"], [80, "feature-engineering-motivation"]], "Feature importances": [[23, "feature-importances"], [39, "feature-importances"], [79, "feature-importances"]], "Feature importances in linear models": [[23, "feature-importances-in-linear-models"], [23, "id2"], [39, "feature-importances-in-linear-models"], [39, "id2"], [79, "feature-importances-in-linear-models"], [79, "id2"]], "Feature interactions and feature crosses": [[24, "feature-interactions-and-feature-crosses"], [40, "feature-interactions-and-feature-crosses"], [80, "feature-interactions-and-feature-crosses"]], "Feature names of transformed data": [[21, "feature-names-of-transformed-data"], [37, "feature-names-of-transformed-data"], [77, "feature-names-of-transformed-data"]], "Feature selection: Introduction and motivation": [[24, "feature-selection-introduction-and-motivation"], [40, "feature-selection-introduction-and-motivation"], [80, "feature-selection-introduction-and-motivation"]], "Feature transformations and the golden rule": [[16, "feature-transformations-and-the-golden-rule"], [72, "feature-transformations-and-the-golden-rule"]], "Feature types": [[21, "feature-types"], [21, "id1"], [37, "feature-types"], [37, "id1"], [77, "feature-types"], [77, "id1"]], "Feature vectors": [[15, "feature-vectors"], [71, "feature-vectors"]], "Figures": [[7, "figures"]], "Filtering a dataframe with [] and df.query()": [[8, "filtering-a-dataframe-with-and-df-query"]], "Final comments and summary": [[19, "final-comments-and-summary"], [43, "final-comments-and-summary"], [75, "final-comments-and-summary"], [83, "final-comments-and-summary"]], "Final comments, summary, and reflection": [[13, "final-comments-summary-and-reflection"], [25, "final-comments-summary-and-reflection"], [26, "final-comments-summary-and-reflection"], [69, "final-comments-summary-and-reflection"], [81, "final-comments-summary-and-reflection"], [82, "final-comments-summary-and-reflection"]], "Final exam": [[89, "final-exam"]], "Final remarks": [[86, "final-remarks"]], "Finding the distances to a query point": [[15, "finding-the-distances-to-a-query-point"], [71, "finding-the-distances-to-a-query-point"]], "Finding the nearest neighbour": [[15, "finding-the-nearest-neighbour"], [71, "finding-the-nearest-neighbour"]], "First deliverables": [[12, "first-deliverables"]], "Forecasting further into the future": [[86, "forecasting-further-into-the-future"]], "Forecasting further into the future on a retail dataset": [[86, "forecasting-further-into-the-future-on-a-retail-dataset"]], "Formulating the problem of recommender systems": [[43, "formulating-the-problem-of-recommender-systems"], [83, "formulating-the-problem-of-recommender-systems"]], "Garbage in, garbage out.": [[24, "garbage-in-garbage-out"], [80, "garbage-in-garbage-out"]], "General advice on finding relevant features": [[24, "general-advice-on-finding-relevant-features"], [80, "general-advice-on-finding-relevant-features"]], "General guidelines": [[6, "general-guidelines"]], "General idea": [[22, "general-idea"], [38, "general-idea"], [78, "general-idea"]], "General idea of k-nearest neighbours algorithm": [[15, "general-idea-of-k-nearest-neighbours-algorithm"], [71, "general-idea-of-k-nearest-neighbours-algorithm"]], "General idea of search and score methods": [[24, "general-idea-of-search-and-score-methods"], [80, "general-idea-of-search-and-score-methods"]], "General questions": [[4, "general-questions"]], "Generalization [video]": [[14, "generalization-video"], [70, "generalization-video"]], "Generalization: Fundamental goal of ML": [[14, "generalization-fundamental-goal-of-ml"], [70, "generalization-fundamental-goal-of-ml"]], "Generalizing to more features": [[18, "generalizing-to-more-features"], [74, "generalizing-to-more-features"]], "Generalizing to unseen data": [[14, "generalizing-to-unseen-data"], [70, "generalizing-to-unseen-data"]], "Geometric view of tabular data and dimensions": [[15, "geometric-view-of-tabular-data-and-dimensions"], [71, "geometric-view-of-tabular-data-and-dimensions"]], "Git": [[10, "git"]], "GitHub Desktop": [[5, "github-desktop"]], "Global average baseline": [[43, "global-average-baseline"], [83, "global-average-baseline"]], "Golden rule violation: Example 1": [[14, "golden-rule-violation-example-1"], [70, "golden-rule-violation-example-1"]], "Golden rule violation: Example 2": [[14, "golden-rule-violation-example-2"], [70, "golden-rule-violation-example-2"]], "Grades": [[12, "grades"]], "Gradient boosted trees [video]": [[22, "gradient-boosted-trees-video"], [38, "gradient-boosted-trees-video"], [78, "gradient-boosted-trees-video"]], "Gradient boosting in sklearn": [[22, "gradient-boosting-in-sklearn"], [38, "gradient-boosting-in-sklearn"], [78, "gradient-boosting-in-sklearn"]], "Grading concerns: time limit": [[6, "grading-concerns-time-limit"]], "Grading scheme": [[89, "grading-scheme"]], "Grading-related questions": [[4, "grading-related-questions"]], "Handling imbalance": [[20, "handling-imbalance"], [76, "handling-imbalance"]], "Here is the workflow we\u2019ll generally follow.": [[14, "here-is-the-workflow-we-ll-generally-follow"], [70, "here-is-the-workflow-we-ll-generally-follow"]], "Hierarchical clustering": [[42, "hierarchical-clustering"]], "Hierarchical clustering [video]": [[26, "hierarchical-clustering-video"], [82, "hierarchical-clustering-video"]], "Homework info & submission guidelines": [[7, null]], "How are we making predictions?": [[18, "how-are-we-making-predictions"], [74, "how-are-we-making-predictions"]], "How can we avoid violating golden rule?": [[14, "how-can-we-avoid-violating-golden-rule"], [70, "how-can-we-avoid-violating-golden-rule"]], "How can we get feature importances for non sklearn models?": [[23, "how-can-we-get-feature-importances-for-non-sklearn-models"], [39, "how-can-we-get-feature-importances-for-non-sklearn-models"], [79, "how-can-we-get-feature-importances-for-non-sklearn-models"]], "How do they work?": [[22, "how-do-they-work"], [38, "how-do-they-work"], [78, "how-do-they-work"]], "How do we carry out feature selection?": [[24, "how-do-we-carry-out-feature-selection"], [40, "how-do-we-carry-out-feature-selection"], [80, "how-do-we-carry-out-feature-selection"]], "How does fit work?": [[13, "how-does-fit-work"], [13, "id2"], [69, "how-does-fit-work"], [69, "id2"]], "How does it work?": [[26, "how-does-it-work"], [82, "how-does-it-work"]], "How does logistic regression calculate these probabilities?": [[18, "how-does-logistic-regression-calculate-these-probabilities"], [74, "how-does-logistic-regression-calculate-these-probabilities"]], "How does predict work?": [[13, "how-does-predict-work"], [69, "how-does-predict-work"]], "How to approximate generalization error?": [[14, "how-to-approximate-generalization-error"], [70, "how-to-approximate-generalization-error"]], "How to ask for help": [[4, null]], "How to carry out cross-validation?": [[16, "how-to-carry-out-cross-validation"], [72, "how-to-carry-out-cross-validation"]], "How to choose n_neighbors?": [[15, "how-to-choose-n-neighbors"], [71, "how-to-choose-n-neighbors"]], "How to pick a model that would generalize better?": [[14, "how-to-pick-a-model-that-would-generalize-better"], [70, "how-to-pick-a-model-that-would-generalize-better"]], "How to submit": [[7, "how-to-submit"]], "How would you do it properly? Enter sklearn pipelines!!": [[31, "how-would-you-do-it-properly-enter-sklearn-pipelines"]], "Hyperparameter alpha of Ridge": [[18, "hyperparameter-alpha-of-ridge"], [74, "hyperparameter-alpha-of-ridge"]], "Hyperparameter optimization": [[29, "hyperparameter-optimization"], [57, "hyperparameter-optimization"]], "Hyperparameter optimization motivation": [[75, "hyperparameter-optimization-motivation"]], "Hyperparameter optimization motivation (video)": [[19, "hyperparameter-optimization-motivation-video"]], "Hyperparameter tuning for the number of clusters": [[25, "hyperparameter-tuning-for-the-number-of-clusters"], [81, "hyperparameter-tuning-for-the-number-of-clusters"]], "Hyperparameters of SVM": [[15, "hyperparameters-of-svm"], [71, "hyperparameters-of-svm"]], "Hyperparameters: the problem": [[19, "hyperparameters-the-problem"], [75, "hyperparameters-the-problem"]], "Identify the transformations we want to apply": [[17, "identify-the-transformations-we-want-to-apply"], [73, "identify-the-transformations-we-want-to-apply"]], "Image classification using KNNs and SVM RBF": [[30, "image-classification-using-knns-and-svm-rbf"]], "ImageNet": [[85, "imagenet"]], "Import": [[87, "import"]], "Importance of scaling": [[18, "importance-of-scaling"], [74, "importance-of-scaling"]], "Important hyperparameters": [[22, "important-hyperparameters"], [38, "important-hyperparameters"], [78, "important-hyperparameters"]], "Important hyperparameters of CountVectorizer": [[17, "important-hyperparameters-of-countvectorizer"], [73, "important-hyperparameters-of-countvectorizer"]], "Important links": [[1, "important-links"]], "Important points to remember": [[25, "important-points-to-remember"], [81, "important-points-to-remember"]], "Imports": [[12, "imports"], [13, "imports"], [14, "imports"], [15, "imports"], [16, "imports"], [17, "imports"], [18, "imports"], [19, "imports"], [20, "imports"], [21, "imports"], [22, "imports"], [23, "imports"], [24, "imports"], [25, "imports"], [26, "imports"], [29, "imports"], [30, "imports"], [31, "imports"], [32, "imports"], [33, "imports"], [34, "imports"], [35, "imports"], [36, "imports"], [37, "imports"], [38, "imports"], [39, "imports"], [40, "imports"], [43, "imports"], [44, "imports"], [57, "imports"], [68, "imports"], [69, "imports"], [70, "imports"], [71, "imports"], [72, "imports"], [73, "imports"], [74, "imports"], [75, "imports"], [76, "imports"], [77, "imports"], [78, "imports"], [79, "imports"], [80, "imports"], [81, "imports"], [82, "imports"], [83, "imports"], [84, "imports"], [85, "imports"], [86, "imports"]], "Imports and LO": [[21, "imports-and-lo"], [75, "imports-and-lo"], [77, "imports-and-lo"], [85, "imports-and-lo"]], "Imports and LOs": [[20, "imports-and-los"], [76, "imports-and-los"]], "Imports and learning outcomes": [[25, "imports-and-learning-outcomes"], [81, "imports-and-learning-outcomes"]], "Imports, Announcements, LOs": [[13, "imports-announcements-los"], [69, "imports-announcements-los"]], "Imports, Announcements, and LO": [[17, "imports-announcements-and-lo"], [18, "imports-announcements-and-lo"], [19, "imports-announcements-and-lo"], [73, "imports-announcements-and-lo"], [74, "imports-announcements-and-lo"]], "Imports, LOs": [[14, "imports-los"], [16, "imports-los"], [23, "imports-los"], [39, "imports-los"], [70, "imports-los"], [72, "imports-los"], [79, "imports-los"]], "Imports, announcements, LO": [[86, "imports-announcements-lo"]], "Imports, announcements, LOs": [[22, "imports-announcements-los"], [78, "imports-announcements-los"]], "Imports, announcements, and LOs": [[15, "imports-announcements-and-los"], [71, "imports-announcements-and-los"]], "Imputation": [[16, "imputation"], [72, "imputation"]], "Imputation and scaling [video]": [[16, "imputation-and-scaling-video"], [72, "imputation-and-scaling-video"]], "Incorporating ordinal feature class_attendance": [[17, "incorporating-ordinal-feature-class-attendance"], [73, "incorporating-ordinal-feature-class-attendance"]], "Incorporating text features": [[31, "incorporating-text-features"]], "Increasing the threshold": [[20, "increasing-the-threshold"], [36, "increasing-the-threshold"], [76, "increasing-the-threshold"]], "Indexing Dataframes": [[8, "indexing-dataframes"]], "Indexing cheatsheet": [[8, "indexing-cheatsheet"]], "Inertia": [[25, "inertia"], [81, "inertia"]], "Initialization of K-Means": [[25, "initialization-of-k-means"], [81, "initialization-of-k-means"]], "Inject randomness in the classifier construction": [[22, "inject-randomness-in-the-classifier-construction"], [38, "inject-randomness-in-the-classifier-construction"], [78, "inject-randomness-in-the-classifier-construction"]], "Input data": [[12, "input-data"], [68, "input-data"]], "Input features X and target y": [[12, "input-features-x-and-target-y"], [68, "input-features-x-and-target-y"]], "Installing Python packages": [[10, "installing-python-packages"]], "Instructional Material": [[0, "instructional-material"]], "Instructors": [[1, "instructors"]], "Interim summary": [[20, "interim-summary"], [23, "interim-summary"], [24, "interim-summary"], [36, "interim-summary"], [39, "interim-summary"], [40, "interim-summary"], [76, "interim-summary"], [79, "interim-summary"], [80, "interim-summary"], [86, "interim-summary"]], "Interpretation of coefficients": [[18, "interpretation-of-coefficients"], [74, "interpretation-of-coefficients"]], "Interpretation of coefficients in linear models": [[18, "interpretation-of-coefficients-in-linear-models"], [74, "interpretation-of-coefficients-in-linear-models"]], "Interpreting coefficients of numeric features": [[23, "interpreting-coefficients-of-numeric-features"], [39, "interpreting-coefficients-of-numeric-features"], [79, "interpreting-coefficients-of-numeric-features"]], "Introduction": [[26, "introduction"], [82, "introduction"]], "Introduction to computer vision": [[85, "introduction-to-computer-vision"]], "Introduction to neural networks": [[85, "introduction-to-neural-networks"]], "Introduction to pandas": [[8, "introduction-to-pandas"]], "Introduction to unsupervised learning": [[25, "introduction-to-unsupervised-learning"], [81, "introduction-to-unsupervised-learning"]], "Is it possible to further improve the scores?": [[87, "is-it-possible-to-further-improve-the-scores"]], "Is stratifying a good idea?": [[20, "is-stratifying-a-good-idea"], [76, "is-stratifying-a-good-idea"]], "Is this a realistic representation of text data?": [[17, "is-this-a-realistic-representation-of-text-data"], [73, "is-this-a-realistic-representation-of-text-data"]], "Is \u201cRelevance\u201d clearly defined?": [[24, "is-relevance-clearly-defined"], [24, "id2"], [24, "id3"], [24, "id4"], [24, "id5"], [24, "id6"], [24, "id7"], [80, "is-relevance-clearly-defined"], [80, "id2"], [80, "id3"], [80, "id4"], [80, "id5"], [80, "id6"], [80, "id7"]], "K-Means algorithm": [[25, "k-means-algorithm"], [81, "k-means-algorithm"]], "K-Means clustering [video]": [[25, "k-means-clustering-video"], [81, "k-means-clustering-video"]], "K-Means example": [[25, "k-means-example"], [81, "k-means-example"]], "K-Means limitations": [[26, "k-means-limitations"], [82, "k-means-limitations"]], "K-Means limitations: Shape of K-Means clusters": [[26, "k-means-limitations-shape-of-k-means-clusters"], [82, "k-means-limitations-shape-of-k-means-clusters"]], "K-Means recap": [[26, "k-means-recap"], [82, "k-means-recap"]], "K-Means: failure case 1": [[26, "k-means-failure-case-1"], [82, "k-means-failure-case-1"]], "K-Means: failure case 2": [[26, "k-means-failure-case-2"], [82, "k-means-failure-case-2"]], "K-Means: failure case 3": [[26, "k-means-failure-case-3"], [82, "k-means-failure-case-3"]], "Key point": [[23, "key-point"], [39, "key-point"], [79, "key-point"]], "LDA topics in social media": [[44, "lda-topics-in-social-media"], [84, "lda-topics-in-social-media"]], "LICENSE": [[0, null]], "Labeled vs. Unlabeled data": [[25, "labeled-vs-unlabeled-data"], [81, "labeled-vs-unlabeled-data"]], "Lag-based features": [[86, "lag-based-features"], [86, "id5"]], "Land acknowledgement": [[89, "land-acknowledgement"]], "Large datasets solve many of these problems": [[19, "large-datasets-solve-many-of-these-problems"], [75, "large-datasets-solve-many-of-these-problems"]], "Late submissions": [[7, "late-submissions"]], "Learned coefficients associated with all features": [[18, "learned-coefficients-associated-with-all-features"], [74, "learned-coefficients-associated-with-all-features"]], "Learned model": [[29, "learned-model"], [57, "learned-model"]], "Learning git": [[5, "learning-git"]], "Learning objectives": [[84, "learning-objectives"], [85, "learning-objectives"], [86, "learning-objectives"]], "Learning outcomes": [[12, "learning-outcomes"], [13, "learning-outcomes"], [14, "learning-outcomes"], [15, "learning-outcomes"], [16, "learning-outcomes"], [17, "learning-outcomes"], [18, "learning-outcomes"], [19, "learning-outcomes"], [20, "learning-outcomes"], [21, "learning-outcomes"], [23, "learning-outcomes"], [24, "learning-outcomes"], [25, "learning-outcomes"], [26, "learning-outcomes"], [68, "learning-outcomes"], [69, "learning-outcomes"], [70, "learning-outcomes"], [71, "learning-outcomes"], [72, "learning-outcomes"], [73, "learning-outcomes"], [74, "learning-outcomes"], [75, "learning-outcomes"], [76, "learning-outcomes"], [77, "learning-outcomes"], [79, "learning-outcomes"], [80, "learning-outcomes"], [81, "learning-outcomes"], [82, "learning-outcomes"]], "Learning outcomes ": [[43, "learning-outcomes"], [83, "learning-outcomes"]], "Least confident cases": [[18, "least-confident-cases"], [74, "least-confident-cases"]], "Lecture 1": [[45, null], [58, null]], "Lecture 10": [[46, null]], "Lecture 10: Class demo": [[37, null]], "Lecture 10: Regression metrics": [[21, null], [77, null]], "Lecture 11": [[47, null], [59, null]], "Lecture 12: Ensembles": [[22, null], [38, null], [78, null]], "Lecture 13: Class demo": [[39, null]], "Lecture 13: Feature importances and model transparency": [[23, null], [79, null]], "Lecture 14: Class demo": [[40, null]], "Lecture 14: Feature engineering and feature selection": [[24, null], [80, null]], "Lecture 15: Class demo": [[42, null]], "Lecture 15: Clustering class demo": [[41, null]], "Lecture 15: K-Means Clustering": [[25, null], [81, null]], "Lecture 16: More Clustering": [[26, null], [82, null]], "Lecture 17: Class demo": [[43, null]], "Lecture 17: Recommender Systems": [[83, null]], "Lecture 18: Class demo": [[44, null]], "Lecture 18: Introduction to natural language processing": [[84, null]], "Lecture 19: Multi-class classification and introduction to computer vision": [[85, null]], "Lecture 1: Course Introduction": [[12, null], [68, null]], "Lecture 2": [[48, null], [60, null]], "Lecture 20: Time series": [[86, null]], "Lecture 2: Terminology, Baselines, Decision Trees": [[13, null], [69, null]], "Lecture 3": [[49, null], [61, 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"resources"], [83, "resources"]], "Ridge": [[18, "ridge"], [74, "ridge"]], "Ridge on the California housing dataset": [[18, "ridge-on-the-california-housing-dataset"], [74, "ridge-on-the-california-housing-dataset"]], "RidgeCV": [[21, "ridgecv"], [37, "ridgecv"], [77, "ridgecv"]], "Root mean squared error or RMSE": [[21, "root-mean-squared-error-or-rmse"], [37, "root-mean-squared-error-or-rmse"], [77, "root-mean-squared-error-or-rmse"]], "SHAP (SHapley Additive exPlanations)": [[23, "shap-shapley-additive-explanations"]], "SHAP (SHapley Additive exPlanations) introduction": [[23, "shap-shapley-additive-explanations-introduction"], [39, "shap-shapley-additive-explanations-introduction"], [79, "shap-shapley-additive-explanations-introduction"]], "SHAP Dependence plot": [[23, "shap-dependence-plot"]], "SHAP Force plot": [[23, "shap-force-plot"]], "SHAP on LGBM model": [[23, "shap-on-lgbm-model"]], "SHAP plots": [[39, "shap-plots"], [79, "shap-plots"]], "SHAP summary plot": [[23, "shap-summary-plot"]], "SHAP waterfall plot": [[23, "shap-waterfall-plot"]], "SHAP: visualizing multiple predictions": [[23, "shap-visualizing-multiple-predictions"]], "SMOTE idea": [[20, "smote-idea"], [76, "smote-idea"]], "SMOTE: Synthetic Minority Over-sampling Technique": [[20, "smote-synthetic-minority-over-sampling-technique"], [76, "smote-synthetic-minority-over-sampling-technique"]], "SVM Regressor": [[15, "svm-regressor"], [71, "svm-regressor"]], "Saving time and scaling products": [[12, "saving-time-and-scaling-products"], [68, "saving-time-and-scaling-products"]], "Scaling": [[16, "scaling"], [72, "scaling"]], "Scaling using scikit-learn\u2019s StandardScaler": [[16, "scaling-using-scikit-learn-s-standardscaler"], [72, "scaling-using-scikit-learn-s-standardscaler"]], "Search over multiple hyperparameters": [[15, "search-over-multiple-hyperparameters"], [71, "search-over-multiple-hyperparameters"]], "Seasonality and trends": [[86, "seasonality-and-trends"]], "Section 101": [[27, null]], "Section 102": [[28, null]], "Section 103": [[56, null]], "Select all of the following statements which are True (iClicker)": [[12, "select-all-of-the-following-statements-which-are-true-iclicker"], [68, "select-all-of-the-following-statements-which-are-true-iclicker"]], "Setting up": [[5, "setting-up"]], "Setting up a virtual environment: Conda environments": [[10, "setting-up-a-virtual-environment-conda-environments"]], "Setting up coding environment": [[10, null]], "Setting up your computer for the course": [[12, "setting-up-your-computer-for-the-course"]], "Short posts/articles": [[9, "short-posts-articles"]], "Sigmoid vs. Softmax": [[85, "sigmoid-vs-softmax"]], "Sign of the coefficients": [[18, "sign-of-the-coefficients"], [74, "sign-of-the-coefficients"]], "Silhouette distance for a sample": [[25, "silhouette-distance-for-a-sample"], [81, "silhouette-distance-for-a-sample"]], "Similarity between examples": [[15, "similarity-between-examples"], [71, "similarity-between-examples"]], "Simple feature engineering for our problem.": [[87, "simple-feature-engineering-for-our-problem"]], "Simple train/test split": [[14, "simple-train-test-split"], [70, "simple-train-test-split"]], "SimpleFeature correlations": [[23, "simplefeature-correlations"], [79, "simplefeature-correlations"]], "Single validation set": [[29, "single-validation-set"], [57, "single-validation-set"]], "Slowest method: nested loop": [[8, "slowest-method-nested-loop"]], "Software": [[0, "software"]], "Some important hyperparameters:": [[22, "some-important-hyperparameters"], [38, "some-important-hyperparameters"], [78, "some-important-hyperparameters"]], "Some quotes on feature engineering": [[24, "some-quotes-on-feature-engineering"], [80, "some-quotes-on-feature-engineering"]], "Some terminology related to trees": [[13, "some-terminology-related-to-trees"], [69, "some-terminology-related-to-trees"]], "Some ways to pick hyperparameters:": [[19, "some-ways-to-pick-hyperparameters"], [75, "some-ways-to-pick-hyperparameters"]], "Sorting a dataframe with df.sort_values()": [[8, "sorting-a-dataframe-with-df-sort-values"]], "Spam/non spam toy example": [[17, "spam-non-spam-toy-example"], [73, "spam-non-spam-toy-example"]], "Specific questions": [[4, "specific-questions"]], "Stacking": [[22, "stacking"], [38, "stacking"], [78, "stacking"]], "Steps to train a classifier using sklearn": [[13, "steps-to-train-a-classifier-using-sklearn"], [69, "steps-to-train-a-classifier-using-sklearn"]], "Stratified Splits": [[20, "stratified-splits"], [76, "stratified-splits"]], "Strengths and weaknesses": [[22, "strengths-and-weaknesses"], [38, "strengths-and-weaknesses"], [78, "strengths-and-weaknesses"]], "Strengths of linear models": [[18, "strengths-of-linear-models"], [74, "strengths-of-linear-models"]], "Submitting on Gradescope": [[7, "submitting-on-gradescope"]], "Summary": [[12, "summary"], [15, "summary"], [22, "summary"], [38, "summary"], [44, "summary"], [68, "summary"], [71, "summary"], [78, "summary"], [84, "summary"], [85, "summary"]], "Summary and reflection": [[14, "summary-and-reflection"], [70, "summary-and-reflection"]], "Summary of linear models": [[18, "summary-of-linear-models"], [74, "summary-of-linear-models"]], "Summary of train, validation, test, and deployment data": [[14, "summary-of-train-validation-test-and-deployment-data"], [70, "summary-of-train-validation-test-and-deployment-data"]], "Summary: Pros and cons": [[26, "summary-pros-and-cons"], [82, "summary-pros-and-cons"]], "Supervised approach to rating prediction": [[43, "supervised-approach-to-rating-prediction"], [83, "supervised-approach-to-rating-prediction"]], "Supervised learning": [[25, "supervised-learning"], [81, "supervised-learning"]], "Supervised learning (Reminder)": [[13, "supervised-learning-reminder"], [69, "supervised-learning-reminder"]], "Supervised learning vs. Unsupervised learning": [[13, "supervised-learning-vs-unsupervised-learning"], [69, "supervised-learning-vs-unsupervised-learning"]], "Supervised machine learning": [[12, "supervised-machine-learning"], [68, "supervised-machine-learning"]], "Support Vector Machines (SVMs) with RBF kernel [video]": [[15, "support-vector-machines-svms-with-rbf-kernel-video"], [71, "support-vector-machines-svms-with-rbf-kernel-video"]], "Support vectors": [[15, "support-vectors"], [71, "support-vectors"]], "Syllabus": [[1, "syllabus"], [89, null]], "TAs": [[1, "tas"], [89, "tas"]], "Tabular data": [[13, "tabular-data"], [69, "tabular-data"]], "Take-home message": [[26, "take-home-message"], [82, "take-home-message"]], "Teaching Team": [[89, "teaching-team"]], "Terminology": [[85, "terminology"]], "Terminology [video]": [[13, "terminology-video"], [69, "terminology-video"]], "Testing your git installation": [[5, "testing-your-git-installation"]], "The Dataset": [[57, "the-dataset"]], "The Netflix prize": [[22, "the-netflix-prize"], [78, "the-netflix-prize"]], "The __ syntax": [[19, "the-syntax"], [34, "the-syntax"], [35, "the-syntax"], [75, "the-syntax"]], "The best features may be dependent on the model you use.": [[24, "the-best-features-may-be-dependent-on-the-model-you-use"], [80, "the-best-features-may-be-dependent-on-the-model-you-use"]], "The golden rule ": [[14, "the-golden-rule"], [70, "the-golden-rule"]], "The random forests classifier": [[22, "the-random-forests-classifier"], [38, "the-random-forests-classifier"], [78, "the-random-forests-classifier"]], "The sigmoid function": [[18, "the-sigmoid-function"], [74, "the-sigmoid-function"]], "The teaching team": [[1, "the-teaching-team"]], "The \u201cfundamental tradeoff\u201d of supervised learning:": [[14, "the-fundamental-tradeoff-of-supervised-learning"], [70, "the-fundamental-tradeoff-of-supervised-learning"]], "The \u201cperfect\u201d spaghetti sauce": [[25, "the-perfect-spaghetti-sauce"], [81, "the-perfect-spaghetti-sauce"]], "Thresholding": [[36, "thresholding"]], "Tokenization": [[44, "tokenization"], [84, "tokenization"]], "Topic modeling": [[44, "topic-modeling"], [84, "topic-modeling"]], "Topic modeling motivation": [[44, "topic-modeling-motivation"], [84, "topic-modeling-motivation"]], "Topic modeling pipeline": [[44, "topic-modeling-pipeline"], [84, "topic-modeling-pipeline"]], "Topic modeling toy example": [[44, "topic-modeling-toy-example"], [84, "topic-modeling-toy-example"]], "Toy datasets": [[13, "toy-datasets"], [69, "toy-datasets"]], "Traditional time series approaches": [[86, "traditional-time-series-approaches"]], "Train/test split for temporal data": [[86, "train-test-split-for-temporal-data"]], "Train/test splits": [[86, "train-test-splits"]], "Train/validation/test split": [[14, "train-validation-test-split"], [70, "train-validation-test-split"]], "Training a supervised machine learning model with X and y": [[12, "training-a-supervised-machine-learning-model-with-x-and-y"], [68, "training-a-supervised-machine-learning-model-with-x-and-y"]], "Training data for the motivating example": [[18, "training-data-for-the-motivating-example"], [74, "training-data-for-the-motivating-example"]], "Training error vs. Generalization error": [[14, "training-error-vs-generalization-error"], [70, "training-error-vs-generalization-error"]], "Training models with transformed data": [[17, "training-models-with-transformed-data"], [73, "training-models-with-transformed-data"]], "Transfer learning": [[85, "transfer-learning"]], "Transformations on the toy data": [[17, "transformations-on-the-toy-data"], [73, "transformations-on-the-toy-data"]], "Transforming the targets": [[21, "transforming-the-targets"], [37, "transforming-the-targets"], [77, "transforming-the-targets"]], "Transparency and explainability of ML models: Motivation": [[23, "transparency-and-explainability-of-ml-models-motivation"], [39, "transparency-and-explainability-of-ml-models-motivation"], [79, "transparency-and-explainability-of-ml-models-motivation"]], "Tree-based ensemble models": [[22, "tree-based-ensemble-models"], [78, "tree-based-ensemble-models"]], "Tree-based models": [[22, "tree-based-models"], [78, "tree-based-models"]], "Tuning alpha hyperparameter of Ridge": [[21, "tuning-alpha-hyperparameter-of-ridge"], [37, "tuning-alpha-hyperparameter-of-ridge"], [77, "tuning-alpha-hyperparameter-of-ridge"]], "Types of errors": [[14, "types-of-errors"], [70, "types-of-errors"]], "Types of machine learning": [[12, "types-of-machine-learning"], [25, "types-of-machine-learning"], [68, "types-of-machine-learning"], [81, "types-of-machine-learning"]], "Types of problems involving time series": [[86, "types-of-problems-involving-time-series"]], "Typical steps to build a supervised machine learning model": [[29, "typical-steps-to-build-a-supervised-machine-learning-model"], [57, "typical-steps-to-build-a-supervised-machine-learning-model"]], "UBC CPSC 330: Applied Machine Learning (2024W1)": [[1, null]], "Ubuntu Users": [[5, "ubuntu-users"]], "Underfitting": [[14, "underfitting"], [70, "underfitting"]], "Underfitting, overfitting, the fundamental trade-off, the golden rule [video]": [[14, "underfitting-overfitting-the-fundamental-trade-off-the-golden-rule-video"], [70, "underfitting-overfitting-the-fundamental-trade-off-the-golden-rule-video"]], "Undersampling": [[20, "undersampling"], [76, "undersampling"]], "Unequally spaced time points": [[86, "unequally-spaced-time-points"]], "Unsupervised learning": [[25, "unsupervised-learning"], [81, "unsupervised-learning"]], "Updates to assignments": [[7, "updates-to-assignments"]], "Use of AI in the course": [[89, "use-of-ai-in-the-course"]], "Use our template to create a repository": [[7, "use-our-template-to-create-a-repository"]], "Using OVR and OVO as wrappers": [[88, "using-ovr-and-ovo-as-wrappers"]], "Using SMOTE": [[20, "using-smote"], [76, "using-smote"]], "Using Silhouette scores to select the number of clusters": [[25, "using-silhouette-scores-to-select-the-number-of-clusters"], [81, "using-silhouette-scores-to-select-the-number-of-clusters"]], "Using multiple metrics in GridSearchCV or RandomizedSearchCV": [[21, "using-multiple-metrics-in-gridsearchcv-or-randomizedsearchcv"], [37, "using-multiple-metrics-in-gridsearchcv-or-randomizedsearchcv"], [77, "using-multiple-metrics-in-gridsearchcv-or-randomizedsearchcv"]], "Using pre-trained models as feature extractor": [[85, "using-pre-trained-models-as-feature-extractor"]], "Using pre-trained models out-of-the-box": [[85, "using-pre-trained-models-out-of-the-box"]], "Using regression metrics with scikit-learn": [[21, "using-regression-metrics-with-scikit-learn"], [37, "using-regression-metrics-with-scikit-learn"], [77, "using-regression-metrics-with-scikit-learn"]], "Viewing the transformed data as a dataframe": [[17, "viewing-the-transformed-data-as-a-dataframe"], [73, "viewing-the-transformed-data-as-a-dataframe"]], "Virtual environment": [[10, "virtual-environment"]], "Visualization": [[9, "visualization"]], "Visualizing the parameter grid as a heatmap": [[19, "visualizing-the-parameter-grid-as-a-heatmap"], [34, "visualizing-the-parameter-grid-as-a-heatmap"], [35, "visualizing-the-parameter-grid-as-a-heatmap"], [75, "visualizing-the-parameter-grid-as-a-heatmap"]], "Warning": [[13, null], [69, null]], "Warnings about feature selection": [[24, "warnings-about-feature-selection"], [24, "id8"], [40, "warnings-about-feature-selection"], [80, "warnings-about-feature-selection"], [80, "id8"]], "Weaknesses": [[22, "weaknesses"], [38, "weaknesses"], [78, "weaknesses"]], "What all transformations we need to apply on the dataset?": [[72, "what-all-transformations-we-need-to-apply-on-the-dataset"]], "What and Why": [[10, "what-and-why"]], "What are git and GitHub?": [[5, null]], "What are the options?": [[16, "what-are-the-options"], [72, "what-are-the-options"]], "What are we exactly learning?": [[18, "what-are-we-exactly-learning"], [74, "what-are-we-exactly-learning"]], "What did we cover?": [[43, "what-did-we-cover"], [83, "what-did-we-cover"]], "What did we learn today?": [[14, "what-did-we-learn-today"], [16, "what-did-we-learn-today"], [17, "what-did-we-learn-today"], [20, "what-did-we-learn-today"], [21, "what-did-we-learn-today"], [70, "what-did-we-learn-today"], [72, "what-did-we-learn-today"], [73, "what-did-we-learn-today"], [76, "what-did-we-learn-today"], [77, "what-did-we-learn-today"]], "What if we apply OHE?": [[17, "what-if-we-apply-ohe"], [73, "what-if-we-apply-ohe"]], "What is Natural Language Processing (NLP)?": [[44, "what-is-natural-language-processing-nlp"], [84, "what-is-natural-language-processing-nlp"]], "What is a recommender system?": [[43, "what-is-a-recommender-system"], [83, "what-is-a-recommender-system"]], "What is clustering?": [[25, "what-is-clustering"], [81, "what-is-clustering"]], "What is feature engineering?": [[24, "what-is-feature-engineering"], [80, "what-is-feature-engineering"]], "What is feature selection?": [[24, "what-is-feature-selection"], [40, "what-is-feature-selection"], [80, "what-is-feature-selection"]], "What is model interpretability?": [[23, "what-is-model-interpretability"], [39, "what-is-model-interpretability"], [79, "what-is-model-interpretability"]], "What is supervised machine learning (ML)?": [[12, "what-is-supervised-machine-learning-ml"], [68, "what-is-supervised-machine-learning-ml"]], "What is \u201cpositive\u201d and \u201cnegative\u201d?": [[20, "what-is-positive-and-negative"], [36, "what-is-positive-and-negative"], [76, "what-is-positive-and-negative"]], "What kind of estimators can we combine?": [[22, "what-kind-of-estimators-can-we-combine"], [38, "what-kind-of-estimators-can-we-combine"], [78, "what-kind-of-estimators-can-we-combine"]], "What to look for in these plots?": [[25, "what-to-look-for-in-these-plots"], [81, "what-to-look-for-in-these-plots"]], "What transformations we need to apply on the dataset?": [[16, "what-transformations-we-need-to-apply-on-the-dataset"]], "What\u2019s the Question?": [[57, "what-s-the-question"]], "What\u2019s the problem?": [[16, "what-s-the-problem"], [72, "what-s-the-problem"]], "When can we use broadcasting?": [[8, "when-can-we-use-broadcasting"]], "When is it OK to do things before splitting?": [[16, "when-is-it-ok-to-do-things-before-splitting"], [72, "when-is-it-ok-to-do-things-before-splitting"]], "When test score is much lower than CV score": [[19, "when-test-score-is-much-lower-than-cv-score"], [75, "when-test-score-is-much-lower-than-cv-score"]], "Which model is doing better in this scenario: SVC or Logistic Regression?": [[36, "which-model-is-doing-better-in-this-scenario-svc-or-logistic-regression"]], "Which model should I use?": [[22, "which-model-should-i-use"], [38, "which-model-should-i-use"], [78, "which-model-should-i-use"]], "Which type of error is more important?": [[20, "which-type-of-error-is-more-important"], [76, "which-type-of-error-is-more-important"]], "Which types of errors would be most critical for the bank to address?": [[36, "which-types-of-errors-would-be-most-critical-for-the-bank-to-address"]], "Why do we need a test set?": [[19, "why-do-we-need-a-test-set"], [75, "why-do-we-need-a-test-set"]], "Why do we want this information?": [[23, "why-do-we-want-this-information"], [39, "why-do-we-want-this-information"], [79, "why-do-we-want-this-information"]], "Why feature selection?": [[24, "why-feature-selection"], [40, "why-feature-selection"], [80, "why-feature-selection"]], "Why machine learning (ML)? [video]": [[12, "why-machine-learning-ml-video"], [68, "why-machine-learning-ml-video"]], "Why model transparency/interpretability?": [[23, "why-model-transparency-interpretability"], [39, "why-model-transparency-interpretability"], [79, "why-model-transparency-interpretability"]], "Why neural networks?": [[85, "why-neural-networks"], [85, "id1"]], "Why not neural networks?": [[85, "why-not-neural-networks"], [85, "id2"]], "Why should we care about recommendation systems?": [[43, "why-should-we-care-about-recommendation-systems"], [83, "why-should-we-care-about-recommendation-systems"]], "Why sparse matrices?": [[17, "why-sparse-matrices"], [73, "why-sparse-matrices"]], "Windows": [[10, "windows"]], "Windows Users": [[5, "windows-users"]], "Word embeddings": [[44, "word-embeddings"], [84, "word-embeddings"]], "Word vectors with spaCy": [[44, "word-vectors-with-spacy"], [84, "word-vectors-with-spacy"]], "Writing a traditional program to predict quiz2 grade": [[13, "writing-a-traditional-program-to-predict-quiz2-grade"], [69, "writing-a-traditional-program-to-predict-quiz2-grade"]], "XGBoost": [[22, "xgboost"], [38, "xgboost"], [78, "xgboost"]], "[Optional] Jupyterlab and Python": [[10, "optional-jupyterlab-and-python"]], "[] notation": [[8, "notation"]], "class_weight=\"balanced\"": [[20, "class-weight-balanced"], [76, "class-weight-balanced"]], "cross_val_score": [[14, "cross-val-score"], [70, "cross-val-score"]], "cross_validate": [[14, "cross-validate"], [70, "cross-validate"]], "fit and transform paradigm for transformers": [[16, "fit-and-transform-paradigm-for-transformers"], [72, "fit-and-transform-paradigm-for-transformers"]], "fit the classifier": [[13, "fit-the-classifier"], [69, "fit-the-classifier"]], "fit, predict , and score summary": [[13, "fit-predict-and-score-summary"], [69, "fit-predict-and-score-summary"]], "iClicker": [[89, "iclicker"]], "iClicker Exercise 10.1": [[21, "iclicker-exercise-10-1"], [77, "iclicker-exercise-10-1"]], "iClicker Exercise 10.2": [[21, "iclicker-exercise-10-2"], [77, "iclicker-exercise-10-2"]], "iClicker Exercise 12.0": [[22, "iclicker-exercise-12-0"], [78, "iclicker-exercise-12-0"]], "iClicker Exercise 12.1": [[22, "iclicker-exercise-12-1"], [38, "iclicker-exercise-12-1"], [78, "iclicker-exercise-12-1"]], "iClicker Exercise 14.1": [[24, "iclicker-exercise-14-1"], [80, "iclicker-exercise-14-1"]], "iClicker Exercise 19.1": [[85, "iclicker-exercise-19-1"]], "iClicker Exercise 2.2 Supervised vs unsupervised": [[13, "iclicker-exercise-2-2-supervised-vs-unsupervised"], [69, "iclicker-exercise-2-2-supervised-vs-unsupervised"]], "iClicker Exercise 2.3 Classification vs regression": [[13, "iclicker-exercise-2-3-classification-vs-regression"], [69, "iclicker-exercise-2-3-classification-vs-regression"]], "iClicker Exercise 2.5: Baselines and decision trees": [[13, "iclicker-exercise-2-5-baselines-and-decision-trees"], [69, "iclicker-exercise-2-5-baselines-and-decision-trees"]], "iClicker Exercise 3.1": [[14, "iclicker-exercise-3-1"], [70, "iclicker-exercise-3-1"]], "iClicker Exercise 3.2": [[14, "iclicker-exercise-3-2"], [70, "iclicker-exercise-3-2"]], "iClicker Exercise 9.1": [[20, "iclicker-exercise-9-1"], [76, "iclicker-exercise-9-1"]], "iClicker Exercise 9.2": [[20, "iclicker-exercise-9-2"], [76, "iclicker-exercise-9-2"]], "k-Nearest Neighbours (k-NNs) [video]": [[15, "k-nearest-neighbours-k-nns-video"], [71, "k-nearest-neighbours-k-nns-video"]], "k-nearest neighbours imputation": [[43, "k-nearest-neighbours-imputation"], [83, "k-nearest-neighbours-imputation"]], "macOS": [[10, "macos"]], "n_iter": [[19, "n-iter"], [34, "n-iter"], [35, "n-iter"], [75, "n-iter"]], "n_jobs=-1": [[19, "n-jobs-1"], [34, "n-jobs-1"], [35, "n-jobs-1"], [75, "n-jobs-1"]], "pandas_profiler": [[21, "pandas-profiler"], [37, "pandas-profiler"], [77, "pandas-profiler"]], "predict the target of given examples": [[13, "predict-the-target-of-given-examples"], [69, "predict-the-target-of-given-examples"]], "predict_proba": [[18, "predict-proba"], [74, "predict-proba"]], "random_state argument": [[14, "random-state-argument"], [70, "random-state-argument"]], "score your model": [[13, "score-your-model"], [69, "score-your-model"]], "sklearn API summary: estimators": [[16, "sklearn-api-summary-estimators"], [72, "sklearn-api-summary-estimators"]], "sklearn API summary: transformers": [[16, "sklearn-api-summary-transformers"], [72, "sklearn-api-summary-transformers"]], "sklearn set_config": [[17, "sklearn-set-config"], [73, "sklearn-set-config"]], "sklearn\u2019s ColumnTransformer": [[73, "sklearn-s-columntransformer"]], "sklearn\u2019s ColumnTransformer (Video)": [[17, "sklearn-s-columntransformer-video"]], "sklearn\u2019s SimpleImputer": [[31, "sklearn-s-simpleimputer"]], "sklearn\u2019s feature_importances_ and permutation_importance": [[23, "sklearn-s-feature-importances-and-permutation-importance"], 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guidelines", "CPSC 330 Python notes", "Reference material", "Setting up coding environment", "Course Learning Objectives", "Lecture 1: Course Introduction", "Lecture 2: Terminology, Baselines, Decision Trees", "Lecture 3: Machine Learning Fundamentals", "Lecture 4: \\(k\\)-Nearest Neighbours and SVM RBFs", "Lecture 5: Preprocessing and sklearn pipelines", "Lecture 6: sklearn ColumnTransformer and Text Features", "Lecture 7: Linear Models", "Lecture 8: Hyperparameter Optimization and Optimization Bias", "Lecture 9: Classification metrics", "Lecture 10: Regression metrics", "Lecture 12: Ensembles", "Lecture 13: Feature importances and model transparency", "Lecture 14: Feature engineering and feature selection", "Lecture 15: K-Means Clustering", "Lecture 16: More Clustering", "Section 101", "Section 102", "Lecture 3: ML Fundamentals Class Demo", "Lecture 4: Class demo", "Lecture 5 and 6: Class demo", "Lectures 7: Class demo", "Lectures 7: Class demo", "Lectures 8: Class demo", "Lectures 8: Class demo", "Lecture 9: Class demo", "Lecture 10: Class demo", "Lecture 12: Ensembles", "Lecture 13: Class demo", "Lecture 14: Class demo", "Lecture 15: Clustering class demo", "Lecture 15: Class demo", "Lecture 17: Class demo", "Lecture 18: Class demo", "Lecture 1", "Lecture 10", "Lecture 11", "Lecture 2", "Lecture 3", "Lecture 4", "Lecture 5", "Lecture 6", "Lecture 7", "Lecture 8", "Lecture 9", "Section 103", "Lecture 3 Demo: ML Fundamentals", "Lecture 1", "Lecture 11", "Lecture 2", "Lecture 3", "Lecture 4", "Lecture 5", "Lecture 6", "Lecture 7", "Lecture 8", "Lecture 9", "Lecture 1: Course Introduction", "Lecture 2: Terminology, Baselines, Decision Trees", "Lecture 3: Machine Learning Fundamentals", "Lecture 4: \\(k\\)-Nearest Neighbours and SVM RBFs", "Lecture 5: Preprocessing and sklearn pipelines", "Lecture 6: sklearn ColumnTransformer and Text Features", "Lecture 7: Linear Models", "Lecture 8: Hyperparameter Optimization and Optimization Bias", "Lecture 9: Classification metrics", "Lecture 10: Regression metrics", "Lecture 12: Ensembles", "Lecture 13: Feature importances and model transparency", "Lecture 14: Feature engineering and feature selection", "Lecture 15: K-Means Clustering", "Lecture 16: More Clustering", "Lecture 17: Recommender Systems", "Lecture 18: Introduction to natural language processing", "Lecture 19: Multi-class classification and introduction to computer vision", "Lecture 20: Time series", "Appendix A: Demo of feature engineering for text data", "Appendix B: Multi-class, meta-strategies", "Syllabus"], "titleterms": {"": [12, 14, 15, 16, 17, 20, 21, 23, 31, 36, 37, 39, 41, 57, 68, 70, 71, 72, 73, 76, 77, 79, 86], "0": [22, 78], "1": [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 34, 35, 38, 43, 45, 58, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85], "10": [21, 37, 46, 77], "101": 27, "102": 28, "103": 56, "11": [47, 59], "12": [22, 38, 78], "13": [23, 39, 79], "14": [24, 40, 80], "15": [25, 41, 42, 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